Data Engineer

• Design, build, and maintain scalable data pipelines and ETL processes to support business analytics and operational needs.
• Collaborate with cross-functional teams to integrate, transform, and make data accessible for analysis and decision-making.
• Implement and optimize data ingestion processes, ensuring efficient data movement across systems.
• Develop and manage Spark-based data processing workflows for real-time and batch processing.
• Write clean, efficient, and well-documented Python code to support data engineering workflows.
• Monitor and troubleshoot data pipelines, ensuring high availability and reliability.
• Drive best practices for data governance, security, and quality to ensure accuracy and consistency.
• Stay up to date with industry trends, tools, and best practices to continuously improve our data architecture and processes
• Mentor team members or lead projects if interested in growing into a leadership role.

• 3+ years of experience as a Data Engineer, working with large-scale data infrastructure.
• Strong proficiency in Python for data processing and automation.
• Deep understanding of distributed data processing architecture and tools such as Spark, Kubernetes and Kafka.
• Experience working with data integration tools.
• Deep understanding of ETL processes and data pipeline orchestration.
• Self-motivated and proactive with a strong ability to learn new technologies quickly.
• A humble yet confident mindset, open to feedback and collaboration.
• Excellent problem-solving skills with a keen attention to detail.

• Experience with cloud platforms such as GCP, AWS, or Azure.
• Familiarity with data warehouse solutions like Snowflake, BigQuery, or Redshift.
• Knowledge of workflow orchestration tools such as Apache Airflow.
• Experienced in deploying and maintaining complex machine learning models in production environments.
• Prior experience working in a startup environment.
• Knowledge of CI/CD pipelines and test-driven development.

Mploy